Predictive memory and context intelligence API for AI Agents
Ranked #1 on Stanford’s STaRK benchmark with 91%+ retrieval accuracy and <100ms, Papr unifies RAG + memory in one API that reduces AI hallucinations and powers personalized agents. Papr’s predictive engine links and structures context into a vector index + knowledge graph you can query with GraphQL or natural language—great for agents and analytics UIs. With built-in ACLs and permission controls, data stays private and multi-tenant. Available in open-source for a local version or cloud edition.
Papr offers knowledge connections and retrieval capabilities that allow AI agents to access and understand organizational data while maintaining safer automation practices and data governance.
Papr equips AI agents with context intelligence and retrieval systems that enable seamless integration with existing workflows while preserving user history and organizational knowledge.
Papr provides persistent memory and context intelligence to AI agents, enabling them to remember users and improve interactions with each session by retaining historical context and user preferences.
Papr's persistent memory architecture enables AI agents to learn from past interactions and build knowledge connections, allowing agents to provide increasingly relevant and personalized responses with each session.